Cover Image
Read Now

Machine Learning in Tribology

Tribology has been and continues to be one of the most relevant fields, being present in almost all aspects of our lives. The understanding of tribology provides us with solutions for future technical challenges. At the root of all advances made so far are multitudes of precise experiments and an in...

Full description

Bibliographic Details
Main Author: Tremmel, Stephan
Other Authors: Marian, Max
Format: eBook
Language:English
Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
Subjects:
Machine Learning
Unbalanced Datasets
Data Mining
Random Forest
Random Forest Classifier
Condition Monitoring
Rolling Bearing Dynamics
Convolutional Neural Network (cnn)
N/a
Meta-modeling
Fault Data Generation
Reduced Order Modelling
Monitoring
Digital Twin
Neural Networks
Structure-borne Sound
History Of Engineering & Technology / Bicssc
Bert
Technology: General Issues / Bicssc
Remaining Useful Life
Tribo-testing
Rubber Seal Applications
Rolling Bearings
Laser Surface Texturing
Total Knee Replacement
Tribain
Evolutionary Algorithms
Gradient Boosting
Databases
Dynamic Friction
Feature Engineering
Reynolds Equation
Analysis
Prediction
Triboinformatics
Regression
Self-lubricating Journal Bearings
Tribo-informatics
Uhwmpe
Pinn
Generative Adversarial Network (gan)
Artificial Intelligence
Semi-supervised Learning
Natural Language Processing
Gaussian Processes
Artificial Neural Networks
Tribology
Cage Instability
Tensor Decomposition
Optimization
Texturing During Moulding
Amorphous Carbon Coatings
Bearing Fault Diagnosis
Online Access:
Collection: Directory of Open Access Books - Collection details see MPG.ReNa
Description
Summary:Tribology has been and continues to be one of the most relevant fields, being present in almost all aspects of our lives. The understanding of tribology provides us with solutions for future technical challenges. At the root of all advances made so far are multitudes of precise experiments and an increasing number of advanced computer simulations across different scales and multiple physical disciplines. Based upon this sound and data-rich foundation, advanced data handling, analysis and learning methods can be developed and employed to expand existing knowledge. Therefore, modern machine learning (ML) or artificial intelligence (AI) methods provide opportunities to explore the complex processes in tribological systems and to classify or quantify their behavior in an efficient or even real-time way. Thus, their potential also goes beyond purely academic aspects into actual industrial applications. To help pave the way, this article collection aimed to present the latest research on ML or AI approaches for solving tribology-related issues generating true added value beyond just buzzwords. In this sense, this Special Issue can support researchers in identifying initial selections and best practice solutions for ML in tribology.
Item Description:Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
Physical Description:1 electronic resource (208 p.)
ISBN:9783036539812
9783036539829
books978-3-0365-3982-9

Similar Items